A feedforward and feedback integrated lateral and longitudinal driver model for personalized advanced driver assistance systems
A feedforward and feedback integrated lateral and longitudinal driver model for personalized advanced driver assistance systems
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DOI：
10.1016/j.mechatronics.2018.02.007
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发表时间：
2018-04-01
期刊：
影响因子：
3.3
通讯作者：
Su, Hai-jun
中科院分区：
文献类型：
--
作者：
Schnelle, Scott;Wang, Junmin;Su, Hai-jun
高级驾驶辅助系统 (ADAS) 越来越受到人们的关注，因为它们正在量产车辆上实施，并且还在继续开发和研究。这些系统需要与人类驾驶员协作，以提高车辆驾驶安全性和性能。这种合作要求 ADAS 与特定驾驶员合作，并了解人类驾驶员的驾驶行为。为了帮助人类驾驶员和 ADAS 之间的这种合作，驾驶员模型必须能够复制和预测人类驾驶行为并区分不同的驾驶员。本文提出了一种基于人体驾驶模拟器实验开发的横向和纵向组合驾驶员模型，该模型能够通过驾驶员模型参数识别来识别不同的驾驶员行为。横向驾驶员模型由补偿传递函数和预期组件组成，并与单个驾驶员所需路径的设计相结合。纵向驾驶员模型与横向驾驶员模型一起使用相同的期望路径参数，根据与前车的相对速度和相对距离对驾驶员的速度控制进行建模。通过考虑驾驶员基于他/她期望路径的曲率调节他/她的速度的能力，将前馈分量添加到反馈纵向驾驶员模型中。纵向和横向驾驶员模型之间的这种互连允许更少的驾驶员模型参数并提高建模精度。事实证明，所提出的驾驶员模型可以复制各个驾驶员的方向盘角度和速度，以进行各种高速公路操作。
Advanced driver assistance systems (ADAS) are a subject of increasing interest as they are being implemented on production vehicles and also continue to be developed and researched. These systems need to work cooperatively with human drivers to increase vehicle driving safety and performance. Such cooperation requires the ADAS to work with the specific driver with some knowledge of the human driver's driving behavior. To aid such cooperation between human drivers and ADAS, driver models are necessary to replicate and predict human driving behaviors and distinguish among different drivers. This paper presents a combined lateral and longitudinal driver model developed based on human subject driving simulator experiments that is able to identify different driver behaviors through driver model parameter identification. The lateral driver model consists of a compensatory transfer function and an anticipatory component and is integrated with the design of the individual driver's desired path. The longitudinal driver model works with the lateral driver model by using the same desired path parameters to model the driver's velocity control based on the relative velocity and relative distance to the preceding vehicle. A feedforward component is added to the feedback longitudinal driver model by considering the driver's ability to regulate his/her velocity based on the curvature of his/her desired path. This interconnection between the longitudinal and lateral driver models allows for fewer driver model parameters and an increased modeling accuracy. It has been shown that the proposed driver model can replicate individual driver's steering wheel angle and velocity for a variety of highway maneuvers.